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docs: Fix typo

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Sean Barbeau 2022-03-29 16:45:30 -04:00 • committed by GitHub
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@ -408,7 +408,7 @@ Massachusetts Bay Transportation Authority (MBTA)](https://github.com/mbta).
- [gtfs-rt-differential-to-full-dataset](https://github.com/derhuerst/gtfs-rt-differential-to-full-dataset) – Javascript tool to transform a continuous GTFS Realtime stream of `DIFFERENTIAL` incrementality data into a `FULL_DATASET` dump.
- [gtfs-rt-dump](https://github.com/kurtraschke/gtfs-rt-dump) - Converts protocol buffer format to plain text for easy viewing of a GTFS-realtime feed in plain text (for debugging purposes)
- [gtfs-rt-inspector](https://public-transport.github.io/gtfs-rt-inspector/) – Web app to inspect & analyze any (CORS-enabled) GTFS Realtime feed. Open-source on [GitHub](https://github.com/public-transport/gtfs-rt-inspector).
[GTFS Data Pipeline for TfNSW Bus Datasets](https://github.com/teckkean/GTFS-Data-Pipeline-TfNSW-Bus) - A data pipeline developed for the TfNSW's GTFS Static and Realtime datasets. The datasets generated using the pipeline have been used to validate the performance of TfNSW's Transit Signal Priority Request via Public Transport Information and Priority System (PTIPS).
- [GTFS Data Pipeline for TfNSW Bus Datasets](https://github.com/teckkean/GTFS-Data-Pipeline-TfNSW-Bus) - A data pipeline developed for the TfNSW's GTFS Static and Realtime datasets. The datasets generated using the pipeline have been used to validate the performance of TfNSW's Transit Signal Priority Request via Public Transport Information and Priority System (PTIPS).
- [manual-gtfsrt](https://github.com/pailakka/manual-gtfsrt) - A Go-based tool that serves a GTFS-RT feed created from editable JSON.
- [print-gtfs-rt-cli](https://github.com/derhuerst/print-gtfs-rt-cli) – Javascript tool to read a GTFS Realtime feed from stdin, print human-readable or as JSON.
- [transitcast](https://github.com/OpenTransitTools/transitcast) - Uses GTFS and GTFS-RT vehicle position feed generating an estimated transition time it takes for each vehicle to move from scheduled stop to scheduled stop recording these an "observed_stop_time" table. These records can later be used to train a machine learning model to make vehicle travel predictions. Created by TriMet as part of [an FTA IMI project](https://trimet.org/imi/program.htm).